Exact transcript quantification over splice graphs.

ABSTRACT:

Background

The probability of sequencing a set of RNA-seq reads can be directly modeled using the abundances of splice junctions in splice graphs instead of the abundances of a list of transcripts. We call this model graph quantification, which was first proposed by Bernard et al. (Bioinformatics 30:2447-55, 2014). The model can be viewed as a generalization of transcript expression quantification where every full path in the splice graph is a possible transcript. However, the previous graph quantification model assumes the length of single-end reads or paired-end fragments is fixed.

Results

We provide an improvement of this model to handle variable-length reads or fragments and incorporate bias correction. We prove that our model is equivalent to running a transcript quanti

SUBMITTER: Ma C 

PROVIDER: S-EPMC8112020 | biostudies-literature | 2021 May

REPOSITORIES: biostudies-literature